What are the most effective real-time markers of kidney cell damage?

Introduction

NGAL, [TIMP‑2]·[IGFBP7], and KIM-1 are the most effective real-time markers of kidney cell damage, showing high diagnostic accuracy when measured at appropriate time windows and achieving superior performance when combined in multi-marker panels.

Abstract

Real‐time markers for kidney cell damage most consistently report high diagnostic accuracy when measured in the appropriate clinical context and time window. Urinary neutrophil gelatinase‐associated lipocalin (NGAL) rises within 2–6 hours after injury, with studies reporting area under the curve (AUC) values up to 0.91, sensitivities of 84–100%, and specificities as high as 99.5%. Urinary [TIMP‑2]·[IGFBP7] is typically measured at 6–12 hours post‐injury and shows AUC values ranging from 0.70 to 0.94, with reported sensitivities of 42–96% and specificities up to 95%. Urinary kidney injury molecule‑1 (KIM‑1) measured at 12–24 hours post‐insult yields moderate performance (AUC 0.66–0.84) with sensitivities between 75.9% and 91.6% and specificities up to 95.2%.

When combined in multi‐marker panels, these biomarkers often achieve superior performance. For example, panels including NGAL, [TIMP‑2]·[IGFBP7], and cystatin C have produced AUC values as high as 0.98, while combinations with additional markers have reached similar levels of discrimination. These findings, drawn from a diverse set of populations and clinical settings—ranging from cardiac surgery and intensive care to emergency presentations—support the use of NGAL, [TIMP‑2]·[IGFBP7], and KIM‑1 as effective real‐time indicators of kidney cell damage.

Methods

We analyzed 40 sources from an initial pool of 999, using 8 screening criteria. Each paper was reviewed for 5 key aspects that mattered most to the research question.

Papers identified with Elicit search

• n = 999

Papers screened using:

• Real-time Biomarker Detection

• Human Participants

• Diagnostic Performance Assessment

• Appropriate Study Design

• Comparative Assessment

• Clinical Study Setting

• Primary Research Quality

• Clinical Utility Assessment

• n = 999 Papers screened out

• n = 959 Papers included for extraction

Data extraction

Biomarkers investigated

• Study design: Specify the exact type of study design used. Possible types include:

  • Cross-sectional study
  • Prospective multicenter study
  • Case-control study
  • Cohort study

• Participant characteristics:

  • Total number of participants
  • Patient population
  • Age range or mean age
  • Gender distribution

• Biomarkers: List ALL biomarkers examined in the study. Include:

  • Full name of biomarker
  • Source of biomarker (e.g., urine, serum)

• Biomarker performance metrics:

  • Area Under the Curve (AUC)
  • Sensitivity
  • Specificity

• Primary outcome definition:

  • Describe how acute kidney injury (AKI) was defined and diagnosed in the study.

Results

Characteristics of Included Studies

Summary of Study Characteristics:

• Most commonly evaluated biomarkers:

  • Neutrophil gelatinase-associated lipocalin (NGAL):29 studies
  • Kidney injury molecule-1 (KIM-1):21 studies
  • [TIMP-2]·[IGFBP7]:12 studies
  • Interleukin-18 (IL-18):11 studies
  • Liver-type fatty acid-binding protein (L-FABP):8 studies
  • Cystatin C (CysC):14 studies

• AKI definitions used:

  • KDIGO:15 studies
  • AKIN:8 studies
  • RIFLE:3 studies

Combined Biomarker Performance

Several studies reported that combinations of biomarkers outperformed individual markers. Key findings from these studies include:

• Combinations typically included [TIMP-2]·[IGFBP7] and NGAL.

Factors Affecting Biomarker Performance

• Patient population
• Comorbidities
• Timing
• AKI definition

Summary

• The effectiveness of real-time kidney cell damage markers was influenced by clinical context, timing, comorbidities, and methodological factors.
• Standardization of definitions, timing, and analytical methods is needed to optimize and compare biomarker performance across studies.

References

1. Ravi J. Desai, et al. (2022). Kidney Damage and Stress Biomarkers for Early Identification of Drug-Induced Kidney Injury: A Systematic Review.
2. W. Han, et al. (2008). Urinary biomarkers in the early diagnosis of acute kidney injury.
3. J. Koyner, et al. (2010). Urinary biomarkers in the clinical prognosis and early detection of acute kidney injury.
4. A. Bihorac, et al. (2014). Validation of cell-cycle arrest biomarkers for acute kidney injury using clinical adjudication.